High Fidelity Modeling and Two-Time-Scale Analysis for Hospital Inpatient Flow Management

该奖项的目的是开发一个分析框架，称为双时间尺度分析，以预测一类非常规随机网络的时间相关性能指标。 这些网络模型的管理医院住院病人流与病人建模的到来和住院病床作为服务器。这种新的分析框架将克服许多现有方法无法解决的挑战，为大规模集成系统。它们包括（a）到达过程具有周期性到达率，（B）服务时间是内生的，取决于停留时间（LOS）、入院和出院时间，以及（c）与到达率的时间变化相比，服务时间非常长。使用该框架，PI和他的合作者将开发方法来预测单集群和多集群模型的稳态时变平均队列长度和平均等待时间。 所研究的模型处理几个现实的和关键的功能，包括一周的现象，非几何LOS分布，分配延迟。多集群模式有一个新的部分共享机制，其中不同集群之间的共享是由病人的长等待时间触发的，如果成功的话，这项研究将产生一个分析和数值工具，可以评估各种业务和战略政策，包括出院，护士配备和能力规划。该工具可以提供以下方面的见解：（i）有效的住院流程管理，以缓解一天中某些时间的急诊室过度拥挤;（ii）不同病房之间的资源分配，以优化多个以患者为中心的绩效指标;以及（iii）整个医院内以及与其他相关医疗保健系统（如降压护理设施）的协调。

The objective of this award is to develop an analytical framework, known as two-time-scale analysis, to predict time-dependent performance measures for a class of unconventional stochastic networks. These networks model the management of hospital inpatient flow with patients modeled as arrivals and inpatient beds as servers. This new analytical framework will overcome many challenges that cannot be solved by existing methods for large-scale queueing systems. They include (a) the arrival process has a periodic arrival rate, (b) the service times are endogenous, depending on length of stay (LOS), admission and discharge times, and (c) service times are extremely long compared with the time-variations of the arrival rate. Using the framework, the PI and his collaborators will develop methods to predict the time-dependent mean queue length and mean waiting time in steady state for both single- and multi-cluster models. The models under study handle several realistic and critical features including day-of-week phenomena, non-geometric LOS distributions, and allocation delays. The multi-cluster model has a novel partial sharing mechanism, where the sharing among different clusters is triggered by patients' long waiting times.If successful, this research will produce an analytical and numerical tool that can evaluate various operational and strategic policies including discharges, nurse staffing, and capacity planning. The tool can provide insights into (i) efficient inpatient flow management to alleviate emergency department overcrowding during certain hours of a day, (ii) resource allocation among different wards to optimize multiple patient-centric performance measures, and (iii) coordination within the entire hospital and with other related healthcare systems such as step-down care facilities.